Parallel Programming Environment for Cluster Computing

Abstract

In this paper, we present a new model for parallel program development for cluster computing called Data Driven Graph (DDG). DDG automatically analyzes data dependence among tasks, synchronizes data, generates task graphs and schedules. Programming in DDG is easy and reliable; most of work is done automatically by DDG, what does not only minimize the amount of work done by programmers but also removes most frequent errors likes race conditions and deadlocks. The integrated scheduler makes parallel programs in DDG run efficiently. Our experiments demonstrate the simplicity and efficiency of programs written in DDG.